Quantifying Privacy for Privacy Preserving Data Mining
Justin Zhan · 2007
Data privacy is an important issue in data mining. How to protect respondents' data privacy during the data collection and mining process is a challenge to the security and privacy community. In this paper, we describe two schemes for privacy preserving naive Bayesian classification which is one of data mining tasks. More importantly, for each scheme, we present a method to measure data privacy. We finally compare these two methods